System and Method for an Intelligent Framework, Flow, and Agent

The present invention relates to a system and method for an intelligent flow framework to enable and control an artificial intelligence model to define actions or tasks and, more particularly, to a system and method implemented by an intelligent flow framework module that is communicatively coupled to an artificial intelligence module and an interface to deploy intelligent flow agents that independently select, prioritize or generate actions using intelligent flow.

This section describes the technical field in detail and discusses problems encountered in the technical field. Therefore, statements in the section are not to be construed as prior art.

In recent years, artificial intelligence has made impressive progress in natural language processing, with Large Language Models (LLMs) leading the way by transforming how machines interact with humans and revolutionizing various industries through applications such as text generation, machine translation, sentiment analysis, and question-answering systems. The emergence of LLMs has brought a paradigm shift in natural language processing (NLP) by improving the performance of various NLP tasks, such as chatbots, by enabling coherent, contextually relevant responses and fostering new possibilities for creative writing, breaking down language barriers, analyzing customer feedback, improving knowledge retrieval systems, and streamlining support services.

Large language models have made it possible to create systems that can partially or completely improve the workflow of human professional activities such as consulting, coaching, education, assistant help, and various types of services like psychological assistance, sales management, healthcare guidance, and physical education. Examples of implementing LLMs for diverse tasks include ChatGTP, LLaMA, Chameleon, Dolly, etc. However, these implementations face inherent technical limitations that can impact their effectiveness and usability in many user scenarios. The limitations of such implementations include passive agents, short or no memory, no pre-defined or self-generated workflows, limited domain knowledge, and a lack of context, emotions, self-reflection, the social aspect, common sense, reasoning, and creativity. Some of these models lack the ability to handle ambiguity, multi-lingual conversations, and vulnerability to bias. These limitations can affect the ability of LLMs to perform certain tasks, especially those that involve longitudinal goals requiring intermediary prerequisites, such as mental health therapy tasks or missions.

The current limitations with single-input generative artificial intelligence (AI) prevent them from performing long-term missions with defined goals, prioritizing tasks and goals, breaking down goals into a chain of actions, launching parallel execution of tasks and goals, accumulating and turning information into knowledge and intuition, forgetting negative experiences or erroneous information, sharing information and skills, using actions and skills from third parties without modifying an intelligent agent (IA) circuit, and exploring open and closed sources for new actions and skills through training and targeted search. These abilities will allow the AI to perform missions (task graphs) more efficiently and effectively, achieve goals, and adapt to changing circumstances. Therefore, there is a void in the technology domain for a mission or task-driven intelligent flow framework, processes, and agents with intelligent choice.

Therefore, there is a need for a system or method to improve the performance of the existing artificial intelligence system by providing a modular framework that can enable AI models to adapt to different missions by any user having little or no knowledge of the underlying AI model.

The object is solved by independent claims, and embodiments and improvements are listed in the dependent claims. Hereinafter, what is referred to as “aspect”, “design”, or “used implementation” relates to an “embodiment” of the invention and when in connection with the expression “according to the invention”, which designates steps/features of the independent claims as claimed, designates the broadest embodiment claimed with the independent claims.

An object of the present invention is to provide a system with the ability to adapt quickly to changing circumstances and make intelligent decisions to ensure the successful completion of missions/objectives.

Another object of the present invention is to provide a system with a modular architecture to allow for flexible customization and optimization to meet the unique needs of different applications.

Another object of the present invention is to provide a system to manage resources effectively and optimize the performance of the system for completing any mission, task, or objective.

Another object of the present invention is to provide a system to incorporate real-time data feeds and analytics to make informed intelligent decisions based on current conditions.

According to an aspect of the present invention, the system comprises an interface, an artificial module, and an intelligent flow framework module. The intelligent flow framework module is communicatively coupled to the interface and the artificial intelligence module. The intelligent flow framework module is configured to define at least one task based on an event and contextual data.

In an embodiment, according to the present invention, the event includes a prompt, message, signal, API call, or a combination thereof.

In an embodiment, according to the present invention, the intelligent flow framework module comprises an active knowledgebase, a contextual unit, and a user profiling database. The contextual unit includes an emotional module, an artificial conscience module, or any other sub-module required for generating the contextual data. The contextual data includes the current state of an actor, environment, actor history, workflow, or a combination thereof.

In an embodiment, according to the present invention, the intelligent flow framework module is configured to generate a task based on an event received from the interface and contextual data retrieved from at least one of the active knowledgebases, the contextual unit, or the user profiling database.

In an embodiment, according to the present invention, the intelligent flow framework module is configured to monitor the current state of the contextual data.

In an embodiment, according to the present invention, the intelligent flow framework module comprises a confidence module and a parameter module.

In an alternative embodiment, according to the present invention, the intelligent flow framework module is configured to define a mission based on the event, the contextual data, or a combination thereof. The intelligent flow framework module is configured to define the at least one task based on the mission, the event, or the contextual data. The at least one task comprises at least one action, a chain of actions, a graph of actions, a prompt, or a combination thereof.

In yet another embodiment, according to the present invention, the intelligent flow framework module is configured to define and assign the at least one task for an intelligent flow agent. The intelligent flow agent executes the at least one task assigned by the intelligent flow framework module.

In yet another embodiment, according to the present invention, the intelligent flow framework module is configured to observe the current state of the task assigned to the intelligent flow agent. The intelligent flow framework module is configured to interrupt the execution of the task assigned to the intelligent flow agent based on the event, contextual data, a new task defined by the intelligent flow framework module, or a combination thereof.

In another embodiment, according to the present invention, the intelligent flow framework module comprises network adapters to connect with external devices, sensors, communication devices, agents, machine interfaces, or web services.

In an alternative embodiment, according to the present invention, the intelligent flow framework module is configured to transfer the at least one task to a new intelligent flow agent, a network adapter, an external intelligent flow agent, or distribute the at least one task between multiple intelligent flow agents and network adapters depending upon the event, current state of contextual data, a new task defined by the intelligent flow framework module, or a combination thereof.

In another embodiment, according to the present invention, the intelligent flow agent relays the at least one task, the event, or the contextual data to an artificial intelligence module.

In yet another embodiment, according to the present invention, the artificial intelligence module includes a generative learning model. The generative model is any neural network based on a transformer architecture, pre-trained on large datasets of unlabeled text, and able to generate novel human-like text, speech, or visual.

In an embodiment, according to the present invention, the artificial intelligence module is trained on application-specific workflow or dataset. The intelligent flow framework module comprises an intelligent flow designer to enable an actor to set at least one workflow, a rule engine, an action, or a combination thereof.

According to another aspect of the present invention, the present invention provides a method implemented by an intelligent module. The method comprises the steps of: a) receiving an event; b) embedding a contextual data to the event; c) defining at least one task based on the event and the embedded contextual data; and d) assigning the at least one task to at least one intelligent flow agent; wherein the assigning the at least one task includes relaying the task, the event, or the embedded contextual data to an artificial intelligence module.

In an embodiment, according to the present invention, embedding the contextual data includes adding current state of at least one actor, environment, actor history, current workflow, or a combination thereof.

In an embodiment, according to the present invention, the at least one actor is user, human, connector, or a non-human logical structure.

In an alternative embodiment, according to the present invention, the actor is at least one of a sensor capturing an environmental or physical metric, wherein the captured metric is the event.

In another embodiment, according to the present invention, receiving an event includes generating the event based on at least one prompt, message, signal, API call or a combination thereof.

In another embodiment, according to the present invention, defining at least one task includes generating at least one action, chain of actions, graph of actions, a prompt, or a combination thereof.

In yet another embodiment, according to the present invention, relaying the task, the event, or the embedded contextual data to an artificial intelligence module comprises a step of receiving an output from the artificial intelligence module. The output comprises at least one action, a chain of actions, a graph of actions, or a combination thereof.

In yet another embodiment, according to the present invention, the method further comprises the steps of a) receiving an event; b) embedding a contextual data to the event; c) defining a mission based on the event and the embedded contextual data; d) determining available actions to complete the mission; e) generating at least one task based on the determined available actions; and f) selecting at least one task to perform and complete the defined mission based on a confidence level related to the determined available actions.

According to another aspect of the present invention, a system comprises a processor, and a non-transitory storage element. The processor hosts an intelligent flow framework module. The intelligent flow framework module comprises an intelligent flow agent, an active knowledgebase, and a contextual unit. The non-transitory storage element coupled to the processor to store the encoded instructions. The encoded instructions, when implemented by the processor, configure the system to perform the steps of: a) receiving an event; b) embedding a contextual data to the event; c) defining a mission based on the event and embedded contextual data; and d) determining all available actions to complete the mission.

According to another aspect of the present invention, the present invention provides a method implemented by an intelligent flow framework module. The method comprises the steps of: a) receiving at least one threshold-grade contextual data of the actor; b) generating an event based on the at least one contextual data; and c) relaying the event and the contextual data to a generative learning model for determining at least one task; wherein relaying of the event and the contextual data is routed through an intelligent flow agent.

Specific embodiments of the invention will now be described in detail with reference to the accompanying. In the following detailed description of embodiments of the invention, numerous details are set forth in order to provide a thorough understanding of the invention. In other instances, well-known features have not been described in detail to avoid obscuring the invention.

The figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. It should also be noted that, in some alternative implementations, the functions noted/illustrated may occur out of order. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

Since various possible embodiments might be proposed of the above invention and amendments might be made in the embodiments above set forth, it is to be understood that all matter herein described or shown in the accompanying drawings is to be interpreted as illustrative and not to be considered in a limiting sense. Thus, it will be understood by those skilled in the art that although the preferred and alternate embodiments have been shown and described in accordance with the Patent Statutes, the invention is not limited thereto or thereby.

Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification do not necessarily refer to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described, which may be exhibited by some embodiments and not by others. Similarly, various requirements are described, which may be requirements for some embodiments but not all embodiments.

The conventional approach to workflow solutions involves using algorithms to define system behavior, where blocks or steps of the system are connected in a rigid execution sequence with explicit branching conditions. In contrast, the proposed method not only specifies the sequence of flow steps but also allows the model to make an independent choice of which step(s) to perform next. This method is also known as intelligent workflow. The intelligent workflow is created and edited using a web or mobile interface or by training a specialized generative learning model. The following ‘definition of terms’ section provides exemplary definitions and, or examples of key terms involved in the intelligent flow framework, intelligent workflow, and intelligent agent.

Intelligent Flow Framework Module: A system architecture of networked modules or components for generating tasks, events, or missions based on available actions or events for a generative model or intelligent flow agent to choose at least one action, a chain of actions, a graph of actions, a prompt, or a combination thereof.

Intelligent Workflow: A complete set of available actions to serve as a basis for defining a task, mission, event, or an event to be relayed to the generative model to choose at least one action, a chain of actions, a graph of actions, a prompt, or a combination thereof.

Intelligent Flow Agent: Deployed on the intelligent flow framework module to generate an event or execute a task assigned by the intelligent flow framework module. The intelligent flow agent may further be generating the event or making the intelligent choice for the at least one action, a chain of actions, a graph of actions, a prompt, or a combination thereof. Furthermore, the intelligent flow agent, as a part of the intelligent flow framework module, may generate the event and/or make the intelligent choice for at least one action, a chain of actions, a graph of actions, a prompt, or a combination thereof.

Intelligent Choice: Choosing at least one action, a chain of actions, a graph of actions, a prompt, or a combination thereof to complete a defined task or mission. These terms are interchangeably used in the description.

Actor: Actor is at least one of a user, human, connector, or a non-human logical structure connected by the connector.

Event: Event includes a prompt, message, signal, API call, or a combination thereof.

Connector/Network Adapter: Connector/Network adapter is any device, component, module, network element, or logic enabling the receiving of the event from the actor into the system or transmitting event, task, mission, at least one action, a chain of actions, a graph of actions, a prompt, or a combination to another component or module of the system.

Actions: Actions are functions performed by the actors. The actors accept arguments, perform instructions, produce an event and/or return a value or output.

EventQueue: EventQueue is a data structure used in computer programming to manage and process the number of events.

EventHandler: EventHandler executes the number of events stored in the EventQueue.